Network analysis of assistive technology stakeholders in Malawi
Bibliographic record
Abstract
BACKGROUND: Assistive technologies promote participation and quality of life for people with disabilities and other functional limitations. There is a global call to develop and implement policies to improve access to assistive technologies. In response, a stakeholder led initiative in Malawi is working towards the development of such a policy. OBJECTIVE: The objective of this study was to assess the existing network of stakeholders, and the strength of relationship between organizations who deliver assistive products and related services. METHOD: We conducted a survey-based network analysis of assistive technology stakeholder organizations in Malawi. RESULTS: Stakeholders (n = 19) reported a range of connections, from no awareness to collaboration with organizations within the assistive technology network. No single organization or government ministry was most central to the network. International NGOs were less central to the network than local organizations for disabled people, service providers, and ministries. CONCLUSION: The assistive technology stakeholder network in Malawi is distributed, with a range of responsibility across a variety of stakeholders, including three government ministries. An effective assistive technology policy must engage all stakeholders and may benefit from a collective leadership approach that spans the inter-sectoral need for a cohesive assistive technology system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".